Real‐world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities

Abstract Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real‐time, real‐world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK‐air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.


| INTRODUCTION
The burden and cost of allergic and chronic respiratory diseases are increasing worldwide, with most economies struggling to effectively respond. [1][2][3][4] Transforming healthcare systems requires strengthened integrated care using organisational health literacy. For this, digital health may be particularly useful, as it may put the patient at the centre of his/her disease management, promote better monitoring and improve patient education. This is particularly true for noncommunicable diseases, whose burden is expected to increase in the near future. It is therefore essential to know of the available digital health tools for each disease and how can they be further explored to improve their management.
Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences (e.g., 'big data' and artificial intelligence). eHealth, as defined by the World Health Organization (WHO), 5 comprises several components including electronic health records, telehealth and mobile health (mHealth). The latter has been defined as a 'medical and public health practice supported by mobile devices, such as mobile phones'. 6 It includes: (i) equipment/connected medical devices, (ii) mHealth services and (iii) mHealth apps. 7,8 Apps designed for and used in allergic rhinitis (AR) and chronic rhinosinusitis (CRS) may help to better understand these diseases and their management as well as to identify and address some unmet needs. This is particularly important in these chronic diseases which are often trivialised 9 and undertreated, 10,11 both by patients and healthcare providers. However, these new tools first need to be tested for privacy rules, acceptability, usability and cost-effectiveness. In addition, they should be evaluated for their impact on (i) the digital transformation of health, (ii) healthcare delivery and (iii) health outcomes. Given the potential of mHealth tools to enable the digital transformation of health and care, empowering citizens and building a healthier society, 12 it is of great importance to review apps whose data collection tools (e.g., questionnaires) have been validated for the case study chronic conditions of allergic rhinitis (AR) and CRS.
In the present paper, all apps relevant to AR and CRS management retrieved using a market research tool based on an automatic search process will be presented. However, only apps with peer-reviewed published data for a given disease will be reviewed. The application of these tools/apps will be discussed regarding their potential for identifying disease phenotypes based on real-life direct patientcentred data, diagnosis, management and adherence to treatment, as well as for promoting the digital transformation of health and care.

| Identification of mHealth apps
An important challenge for app review studies concerns the lack of automatic standardised search strategies, rendering the identification of potentially relevant apps a time-consuming manual task. 13 Such limitations could be overcome by the development of automatic methods for app screening. Recently, such methods have been described for breast cancer, 14 AR, 15 urticaria 16 and anaphylaxis 8 apps. Automatic methods for app screening also have the advantage of running screening processes more frequently than manual approaches and at an increased speed, and of potentially identifying relevant apps whose name and icon are not obvious.
The method used for the identification of relevant mHealth apps in rhinitis has been described elsewhere. In this review, we will focus on (i) the four apps identified by that study as having associated scientific publications for AR 15 as (ii) two additional apps for which scientific publications were subsequently identified. In brief, an app screening programme capable of performing searches in app stores without any human intervention has been developed for searching for AR apps using JavaScript, 15,17 a commonly used programming language that allows searches of dynamic content on web pages. 18 The screening programme builds upon two opensource packages. 19 On the other hand, relevant apps in CRS had not been previously identified. In this study, we used the aforementioned app screening programme to scrape Apple App and Google Play stores 20
T A B L E 1 Apps relevant for allergic rhinitis and rhinosinusitis management and with published data on rhinitis Among the 17 apps retrieved for CRS, only one had published data for this disease, namely Galenus Health (with a single identified paper) (Table 2). However, in this paper, the app is labelled as MySinusitisCoach, developed using the MASK-air ® structure. 58 A new app designed by members of the Mayo Clinic has been developed, but solely tested in a pilot study assessing 10 participants. 62 Kagen Air was only presented in a review paper and was therefore excluded. 63

| Limitations of the followed approaches
We used a small set of search terms, which may correspond to a restrictive approach. Nevertheless, these terms were chosen by the Allergic Rhinitis and its Impact on Asthma (ARIA) expert group.
Moreover, in performing a PubMed search for 'apps', 'mHealth', 'eHealth' AND 'rhinitis' or 'rhinosinusitis', we did not find any other apps other than the ones included in this review. Only two languages were searched and some apps may exist in other countries (e.g., in Polish, the Apsik and Dzienniki Alergika apps are available). However, only one app has been described in articles available in PubMed. 55 An additional limitation is that some apps may not have used the name they are currently using. This is, for example, the case for Galenus Health which was labelled MySinusitisCoach. 58

Methods for statistical analysis
To account for noise when identifying clusters of homogenous patients, the authors applied the fuzzy k-medoids algorithm to the obtained functional coefficients. By the B-spline basis system, these coefficients allowed continuous smoothing functions to be found, synthesising the general trend of the observed data. 23 3. seasons. 73 The TRL has been assessed for the MASK-air ® app by MASK-air ® members (TRL9). 74 MASK-air ® is CE1 registered and follows the GDPR. 60  Scientific studies using PHD/Pollen App symptom data first undergo a filtering process to assure the inclusion of exclusively qualitative data (e.g., for more seasons, with a certain number of entries per user) leading to robust results. 36,37,43,81,82 The datasets have been analysed using not only statistical methods but also computational intelligence methods like Self Organising Maps. 36,37,43,82 Pollen App consists of three main parts: information, symptom documentation and medical assistance. Information is given concerning the pollen load including a personal allergy risk, the daily pollen load for various aeroallergens, forecast maps based on different models, as well as a dictionary with information on the most important allergenic plants. Symptom documentation is made in the pollen diary (Patient's Hay fever diary) and adapts the forecasts automatically if used (personal pollen information and allergy risk). 93 Medical assistance concerns information on doctors in the vicinity, therapy recommendation situations for no-, low-, medium-and high-risk burden as well as a symptom report (available since 2021) that can be shared with the patient's doctor.

Acceptability of MASK-air ® by patients
Forecast data were part of the scientific research besides the exploitation of the symptom data. Nine freely available apps delivering pollen information and pollen forecasts had been tested with a focus on their prediction of the pollen load in the 2016 grass pollen season (Table 3). 48 For six apps, the rates of correct pollen forecasts were around 50%, with Pollen App displaying the highest. Only two apps provided sufficiently accurate forecasts for the "readiness to flower" for grasses.

| Rhinosinusitis
There is only one mobile app within the defined criteria for CRS, High. This hypothesis-generating study was confirmed by classical epidemiologic studies, [94][95][96] showing that it is important to consider ocular symptoms in severe asthma 95 and that the severity of individual allergic diseases increases with the number of allergic morbidities. 97 These findings were reinforced using computational analyses suggesting that there are common pathways in multimorbidity. 98 Quantification of the burden of pollen allergy was performed in Austria and Germany over 10 years using electronically-generated symptom data from PHD. 54 Four different symptom score calculation methods were applied to the datasets. This study did not detect significant differences between the various methods of symptom score calculation. Nasal symptoms determined about 40% of the scores.
Grass pollen-triggered allergic symptoms vary within the season. 89  In the Mediterranean area, patients with pollen-induced AR are often polysensitised, rendering their assessment complex for aerobiologists and physicians. AllergyMonitor ® was used to improve the precision of diagnosing pollen allergy using daily symptom monitoring and graphical representations of airborne pollen data. 47 Unfortunately, diagrams illustrating daily pollen concentrations from many sources in parallel make the interpretation of each of these curves difficult. This problem may be solved by using curves based on the cumulative transformation of pollen data using AllergyMonitor ® . 105

| Impact of air pollution on rhinitis
Several studies have suggested an interaction between air pollution and pollen exposure, with an impact on allergy symptoms. However, large studies with real-life data have not been available until recently.
In the POLLAR study, 41 Using Pollen App, associations between symptoms, grass, birch or ragweed pollen levels, air quality and meteorological data (temperature, relative humidity) were studied for the metropolis of Vienna. 46 Only ozone was significantly associated with symptom scores in birch, grass and ragweed pollen seasons. Further analyses in a model with meteorological data showed that the effect estimates of ozone were attenuated, but remained significant for the grass pollen season.

| Aetiological diagnosis of seasonal allergic rhinitis
The The results showed that (i) A hypothesis-driven score based on MASK-air ® data was highly correlated with all instruments of quality- of-life and work tested, and had high concurrent validity and testretest reliability; (ii) Several data-driven scores, in particular those based on cluster analyses, had a slightly higher level of correlation with identified endpoints; (iii) These results have been found to be highly reproducible across all tested regions (nine countries). 44

| Indication of allergen immunotherapy
The efficacy of AIT depends on the precise identification of the

| Allergen immunotherapy
Real-world data are available for AIT in the MASK-air ® database. 108 A proof-of-concept study has compared days of participants with AIT versus days of participants without AIT on VAS global allergy symptoms and VAS work. A total of 317,176 days were analysed, of which 11.4% involved AIT users. Lower median VAS global allergy symptoms and VAS work levels were observed for participants under AIT and were compared to the levels on days without treatment, with monotherapy or with polytherapy. This enabled us to better understand the role of AIT in real life ( Figure 1). Nevertheless, further studies are required.

| Understanding adherence
mHealth may help to better understand adherence to treatment. 109 Following a pilot study in less than 3000 AR patients, 36  indicating that adherence to AR treatment is low. This study proposed an approach for measuring retrospective adherence based on an app, representing a novel approach for analysing the behaviour of medication-taking in a real-world setting. 111

| Improving adherence
mHealth may improve adherence to treatment in chronic diseases.
Children and adolescents (5-18 years) with moderate-to-severe seasonal AR to grass pollen, requiring a daily INCS administration, were recruited in April 2013. 22 Participants were randomised to AllergyMonitor ® or to usual care (no diary) and followed up until 15 June 2013. Intra-nasal mometasone use, expressed as both optimal adherence rate and average daily use, was higher in the AllergyMonitor ® group than in usual care. Disease knowledge improved among the patients using AllergyMonitor ® but not among the controls.
However, no differences were observed at baseline and at follow-up visits in the reported severity of disease, nasal flow and quality of life.
This was due to an unexpected low temperature and pollen exposure during the observation period.
In another study on AllergyMonitor ® in 67 patients, the adherence to daily symptom monitoring remained high (>80%) throughout several weeks when prescribed and thoroughly explained by the treating doctor. Furthermore, app use was associated with improved adherence to symptomatic drugs and AIT. 21

| ASSESSMENT OF THE ECONOMIC BURDEN OF AR AND COST-EFFECTIVENESS OF MANAGEMENT STRATEGIES
Allergic rhinitis is a burdensome condition, with an important impact on work 112 and school productivity. 113 MASK-air ® can be used to quantify this impact, as it includes questions assessing the daily impact of AR symptoms on work productivity (VAS Work) and on school performance. In addition, the WPAI-AS -which quantifies the impact of allergy on work and activities -can be answered optionally in MASK-air ® . MASK-air ® enables the estimation not only of indirect costs resulting from loss of work productivity, but also of direct costs resulting from AR medication and AIT use. A monthly question asking the user whether he/ she had an outpatient visit related to AR during the previous month could help to further improve the estimation of direct costs related to AR.
MASK-air ® also includes EQ-5D, whose scores can be converted into utilities (standardised measures of preferences that patients have for health status) for many of the countries where MASK-air ® is available. 114 Such a feature may be particularly useful for performing cost-utility analyses, in which interventions are compared regarding their costs and also their effectiveness adjusted for patients' preferences.

| NEXT-GENERATION CARE PATHWAYS FOR THE DIGITAL TRANSFORMATION OF HEALTH CENTRED AROUND THE PATIENT
As an example of chronic disease care, MASK, in collaboration with professional and patient organisations in the field of allergy and airway diseases, proposes real-life care pathways centred around the patient with AR and/or asthma multimorbidity. 115 It uses mHealth to monitor environmental exposure 116  On the other hand, connecting daily apps to medications (e.g., inhalers) or diagnostic tests (e.g., spirometry for asthma) may open the possibility of a more personalised monitoring of patients with AR or CRS. Furthermore, personalisation increases motivation to continuously use mHealth and eHealth apps and may improve adherence.
Other issues meriting discussion concern their certification, reimbursement, interoperability and quality control. Only a small fraction of available apps have published scientific results, with the content veracity of the remaining ones pending assessment.
Among the 1500 apps retrieved for AR and the several hundred retrieved for CRS, only a handful were selected for review, including three multilingual apps and two using a single language in AR. That is, while there are several apps claiming to be health-related, only a few have been studied in a relevant manner, prompting the need for some quality control over health-related apps. This may not only concern AR and CRS but also other chronic diseases.
Apps studied in a relevant manner were found to be of interest in